Computational Disorder Analysis in Ethylene Response Factors Uncovers Binding Motifs Critical to Their Diverse Functions
Bibliographic record
Abstract
APETALA2/ETHYLENE RESPONSE FACTOR transcription factors (AP2/ERFs) play crucial roles in adaptation to stresses such as those caused by pathogens, wounding and cold. Although their name suggests a specific role in ethylene signalling, some ERF members also co-ordinate signals regulated by other key plant stress hormones such as jasmonate, abscisic acid and salicylate. We analysed a set of ERF proteins from three divergent plant species for intrinsically disorder regions containing conserved segments involved in protein-protein interaction known as Molecular Recognition Features (MoRFs). Then we correlated the MoRFs identified with a number of known functional features where these could be identified. Our analyses suggest that MoRFs, with plasticity in their disordered surroundings, are highly functional and may have been shuffled between related protein families driven by selection. A particularly important role may be played by the alpha helical component of the structured DNA binding domain to permit specificity. We also present examples of computationally identified MoRFs that have no known function and provide a valuable conceptual framework to link both disordered and ordered structural features within this family to diverse function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".